Exploring E-Commerce Product Experience Based on Fusion Sentiment Analysis Method
With the speedy development of e-commerce, a growing number of customers tend to share their subjective perceptions of the product or service on the Internet. This phenomenon makes the commercial value of online reviews increasingly prominent. In this context, how to gain insights into consumers&...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9919154/ |
| _version_ | 1811234533153964032 |
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| author | Huaqian He Guijun Zhou Shuang Zhao |
| author_facet | Huaqian He Guijun Zhou Shuang Zhao |
| author_sort | Huaqian He |
| collection | DOAJ |
| description | With the speedy development of e-commerce, a growing number of customers tend to share their subjective perceptions of the product or service on the Internet. This phenomenon makes the commercial value of online reviews increasingly prominent. In this context, how to gain insights into consumers’ perceptions and attitudes from massive comments has become a hot-button topic. Addressing this requirement, this paper developed a fusion sentiment analysis method combining textual analysis techniques with machine learning algorithms, aiming to mine online product experience. The method mainly consists of three steps. Firstly, inspired by the sensitivity of sentiment dictionary to emotional information, we utilize the dictionary to extract sentiment features. Afterward, the SVM algorithm is adopted to identify sentiment polarities of reviews. Based on this, sentiment topics are extracted from reviews through the LDA model. Furthermore, to avoid the omission of emotional information, the dictionary is extended based on semantic similarity. Meanwhile, in this research, the fact that words in reviews have unequal sentiment contribution, which has been neglected in existing studies, is taken into account. Specifically, we introduce the weighting method to measure the sentiment contribution. Finally, the investigation of consumers’ reading experiences of online books on Amazon has verified the feasibility and validity of the method. The results demonstrate that the method accurately determines reviews’ emotional tendencies and captures elements affecting reading experiences from reviews. Overall, the research provides an effective way to mine online product experience and track customers’ demands, thereby strongly supporting future product improvement and marketing strategy optimization. |
| first_indexed | 2024-04-12T11:38:58Z |
| format | Article |
| id | doaj.art-bc4f27d932e840f18421e3507e02fae7 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-12T11:38:58Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-bc4f27d932e840f18421e3507e02fae72022-12-22T03:34:45ZengIEEEIEEE Access2169-35362022-01-011011024811026010.1109/ACCESS.2022.32147529919154Exploring E-Commerce Product Experience Based on Fusion Sentiment Analysis MethodHuaqian He0https://orcid.org/0000-0002-4477-5380Guijun Zhou1https://orcid.org/0000-0003-0050-6266Shuang Zhao2https://orcid.org/0000-0002-0431-1071School of Foreign Languages, Northeast Normal University, Changchun, ChinaSchool of Foreign Languages, Northeast Normal University, Changchun, ChinaSchool of Foreign Languages, Northeast Normal University, Changchun, ChinaWith the speedy development of e-commerce, a growing number of customers tend to share their subjective perceptions of the product or service on the Internet. This phenomenon makes the commercial value of online reviews increasingly prominent. In this context, how to gain insights into consumers’ perceptions and attitudes from massive comments has become a hot-button topic. Addressing this requirement, this paper developed a fusion sentiment analysis method combining textual analysis techniques with machine learning algorithms, aiming to mine online product experience. The method mainly consists of three steps. Firstly, inspired by the sensitivity of sentiment dictionary to emotional information, we utilize the dictionary to extract sentiment features. Afterward, the SVM algorithm is adopted to identify sentiment polarities of reviews. Based on this, sentiment topics are extracted from reviews through the LDA model. Furthermore, to avoid the omission of emotional information, the dictionary is extended based on semantic similarity. Meanwhile, in this research, the fact that words in reviews have unequal sentiment contribution, which has been neglected in existing studies, is taken into account. Specifically, we introduce the weighting method to measure the sentiment contribution. Finally, the investigation of consumers’ reading experiences of online books on Amazon has verified the feasibility and validity of the method. The results demonstrate that the method accurately determines reviews’ emotional tendencies and captures elements affecting reading experiences from reviews. Overall, the research provides an effective way to mine online product experience and track customers’ demands, thereby strongly supporting future product improvement and marketing strategy optimization.https://ieeexplore.ieee.org/document/9919154/E-commerce product experiencefusion methodmachine learningsentiment analysissentiment dictionary |
| spellingShingle | Huaqian He Guijun Zhou Shuang Zhao Exploring E-Commerce Product Experience Based on Fusion Sentiment Analysis Method IEEE Access E-commerce product experience fusion method machine learning sentiment analysis sentiment dictionary |
| title | Exploring E-Commerce Product Experience Based on Fusion Sentiment Analysis Method |
| title_full | Exploring E-Commerce Product Experience Based on Fusion Sentiment Analysis Method |
| title_fullStr | Exploring E-Commerce Product Experience Based on Fusion Sentiment Analysis Method |
| title_full_unstemmed | Exploring E-Commerce Product Experience Based on Fusion Sentiment Analysis Method |
| title_short | Exploring E-Commerce Product Experience Based on Fusion Sentiment Analysis Method |
| title_sort | exploring e commerce product experience based on fusion sentiment analysis method |
| topic | E-commerce product experience fusion method machine learning sentiment analysis sentiment dictionary |
| url | https://ieeexplore.ieee.org/document/9919154/ |
| work_keys_str_mv | AT huaqianhe exploringecommerceproductexperiencebasedonfusionsentimentanalysismethod AT guijunzhou exploringecommerceproductexperiencebasedonfusionsentimentanalysismethod AT shuangzhao exploringecommerceproductexperiencebasedonfusionsentimentanalysismethod |